AI Analysis
Final verdict: SAFE
The package appears to have legitimate functionality with minor risks. While the use of shell commands is concerning, there are no signs of malicious intent, and the maintainer's profile does not raise additional alarms.
- Low obfuscation and credential risks
- Potential for shell command injection
- Incomplete maintainer profile
Per-check LLM notes
- Network: The network call to PubChem suggests legitimate use of external APIs for chemical information retrieval.
- Shell: Executing shell commands can be risky if not properly sanitized; it may indicate potential for arbitrary command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has an incomplete profile and seems new or inactive, but no other red flags are present.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
" try: response = requests.get( "https://pubchem.ncbi.nlm.nih.gov/rest/pug/comp
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 2.0
Found 1 shell execution pattern(s)
ure its output.""" return subprocess.run( [command, *args], capture_output=True, text=True, c
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: hezhiang.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository HzaCode/ChemInformant appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with ChemInformant
Create a Python-based mini-application named 'ChemistryResearchTool' that leverages the 'ChemInformant' package to facilitate chemical research and analysis. This tool will serve as an intuitive interface for researchers to query PubChem's extensive database of chemical compounds and retrieve relevant information for their studies. The application should have the following functionalities: 1. **Compound Search**: Users should be able to input a chemical compound's name or CAS number and receive detailed information such as molecular formula, structure, and properties. 2. **Structure Visualization**: Integrate a feature that allows users to visualize the 3D structure of the compound using data retrieved from PubChem. This could involve generating a static image or even an interactive 3D model if feasible. 3. **Property Analysis**: Provide an analysis of key chemical properties like melting point, boiling point, and solubility. Use these properties to suggest potential applications or limitations of the compound in various fields of study. 4. **Machine Learning Workflow Integration**: Implement a basic machine learning model that predicts certain chemical properties based on historical data retrieved through 'ChemInformant'. This could help in identifying trends or patterns that might not be immediately apparent from raw data alone. 5. **Data Export**: Allow users to export the retrieved and analyzed data into common file formats like CSV or JSON for further analysis in other software tools. To achieve these goals, utilize the 'ChemInformant' package to optimize data retrieval from PubChem's API, ensuring efficient and accurate fetching of chemical information. Additionally, consider integrating other Python libraries such as Matplotlib for plotting properties, PyMOL for 3D visualization, and Scikit-learn for implementing the machine learning model. Your task is to design and implement this tool, providing clear documentation and examples for each functionality to ensure ease of use for researchers and chemists.